Data-Driven Iterative Learning Control for Discrete-Time Systems

Beschreibung

This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system’s output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system’s output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.

Focuses on data-driven iterative learning control for discrete-time systems Proposes systematic procedures for the iterative dynamic linearization method Proposes systematic procedures for design and analysis of data-driven optimal iterative learning control

Autor*in

Ronghu Chi

Themen in »Data-Driven Iterative Learning Control for Discrete-Time Systems«

Data-driven Control Iterative Learning Control Optimal ILC Point-to-Point ILC Terminal ILC Package Dropouts Constrained Systems Varying Trial Length Quantized Systems Event-triggered Mechanism Extended State Observer

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“The DDILC presented in this monograph has three main contributions, i.e., the robustness against nonrepetitive uncertainties is improved, a good transient response is realized, and a rigorous theoretical analysis for the stability, convergence, and robustness is provided. Some new concepts of iterative pseudo-partial derivative and iterative pseudo-gradient as well as a novel analysis method using the contraction mapping principle and iterative dynamic linearization are introduced well and systematically studied, which makes the book well worth reading … .” (Maobin Lu, Mathematical Reviews, July, 2024)

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